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        <h1 id="NET-中的异步-多线程-并发处理-3-并行处理"><a href="#NET-中的异步-多线程-并发处理-3-并行处理" class="headerlink" title=".NET 中的异步/多线程/并发处理(3) - 并行处理"></a>.NET 中的异步/多线程/并发处理(3) - 并行处理</h1><p>本文核心：</p>
<ol>
<li>如何使用户交互界面具有良好的响应？</li>
<li>如何最大化利用CPU的性能？</li>
</ol>
<p>针对上面两个问题，根据 .NET Guide 中的 <a href="https://docs.microsoft.com/en-us/dotnet/standard/parallel-processing-and-concurrency" target="_blank" rel="noopener">Parallel Processing, Concurrency, and Async Programming in .NET</a> 的内容，进一步深入理解 .NET 的下面三种机制：</p>
<ol>
<li>异步编程</li>
<li>并行处理</li>
<li>多线程与线程同步机制。</li>
</ol>
<h2 id="并行处理"><a href="#并行处理" class="headerlink" title="并行处理"></a>并行处理</h2><p>许多个人电脑和工作站都有多个CPU内核，可以同时执行多个线程。预计不久的将来，计算机将拥有更多的内核。为了充分利用现在和未来的硬件，您可以并行化您的代码以跨多个处理器执行。在过去，并行化需要对线程和锁进行低级处理。.NET Framework 4 通过提供新的运行时，新的类库类型和新的诊断工具来增强对并行编程的支持。这些特性简化了并行开发，因此您可以编写高效，细化和可扩展的并行代码，而无需直接与线程或线程池一起工作。以下插图提供了.NET Framework 4 中并行编程体系结构的高级概述。</p>
<p><img src="/my_blog/2018/06/24/DotNet-Guide-Parallel-Programming/tpl-architecture.png" alt="TPL-Architecture"></p>
<h3 id="Task-Parallel-Library-TPL"><a href="#Task-Parallel-Library-TPL" class="headerlink" title="Task Parallel Library (TPL)"></a>Task Parallel Library (TPL)</h3><p>TPL 是 System.Threading 和 System.Threading.Tasks 命名空间中的一组公共类型和 API。TPL动态调整并发度以最有效地使用所有可用的处理器。另外，TPL替你完成一般的并行处理任务中的：任务分派，ThreadPool上线程的调度，取消功能的支持，状态管理以及其他低级细节。通过使用TPL，您可以最大限度地提高代码的性能，同时专注于您的程序旨在完成的工作。</p>
<p>但是，并非所有代码都适合并行化;例如，如果循环在每次迭代中仅执行少量工作，或者它不会运行多次迭代，那么并行化的开销会导致代码运行速度更慢。此外，像任何多线程代码一样的并行化会增加程序执行的复杂性。尽管TPL简化了多线程场景，但起码要对线程概念（例如线程锁，死锁和竞态条件）有基本的了解，以便您可以有效地使用TPL。</p>
<p><code>竟态条件（race condition）：当两个或更多线程可以访问共享数据并尝试同时更改共享数据时，会发生争用情况。</code></p>
<h3 id="Data-Parallelism"><a href="#Data-Parallelism" class="headerlink" title="Data Parallelism"></a>Data Parallelism</h3><pre><code>// Sequential version            
foreach (var item in sourceCollection)
{
    Process(item);
}

// Parallel equivalent
Parallel.ForEach(sourceCollection, item =&gt; Process(item));
</code></pre><p><em>Data Parallelism</em> 是指对源集合或数组中的元素并行（即并行）执行相同操作的场景。在数据并行操作中，对源集合进行分区，以便多个线程可以同时在不同的分段上运行。</p>
<p>任务并行库（TPL）通过System.Threading.Tasks.Parallel类支持数据并行性。此类提供for和foreach循环的基于方法的并行实现（For和For Each在Visual Basic中）。您可以编写Parallel.For或Parallel.ForEach循环的循环逻辑，就像编写循环循环一样。您不必创建线程或排队工作项目。在基本循环中，您不必锁定。 TPL为您处理所有低级别工作。有关使用Parallel.For和Parallel.ForEach的详细信息，请下载用于并行编程的模式：使用.NET Framework 4了解和应用并行模式。以下代码示例显示了一个简单的foreach循环及其并行等效。</p>
<p>当并行循环运行时，TPL分割数据源，以便循环可以同时在多个部分上运行。在幕后，Task Scheduler 根据系统资源和工作负载对任务进行分区。如果可能，如果工作负载变得不平衡，则调度程序会在多个线程和处理器之间重新分配工作。</p>
<p>Parallel.For和Parallel.ForEach方法都有几个重载，这些重载允许您停止或中断循环执行，监视其他线程上的循环状态，维护线程本地状态，定位线程本地对象，控制并发程度，等等。启用此功能的助手类型包括ParallelLoopState，ParallelOptions，ParallelLoopResult，CancellationToken和CancellationTokenSource。</p>
<p>具有声明式或查询式语法的数据并行性由PLINQ支持。有关更多信息，请参阅并行LINQ（PLINQ）。</p>
<h4 id="当循环体的计算比较简单时的处理办法"><a href="#当循环体的计算比较简单时的处理办法" class="headerlink" title="当循环体的计算比较简单时的处理办法"></a>当循环体的计算比较简单时的处理办法</h4><p>当 Parallel.Foreach 循环体的计算比较简单时，它可能比等效的顺序循环执行得更慢。 性能降低是由于分区数据所涉及的开销以及在每次循环迭代中调用委托的成本造成的。 为了解决这种情况，Partitioner类提供了Partitioner.Create方法，该方法使您可以为委托体提供一个顺序循环，以便每个分区仅调用一次委托，而不是每次迭代一次调用一次。</p>
<pre><code>using System;
using System.Collections.Concurrent;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;

class Program
{
    static void Main()
    {

        // Source must be array or IList.
        var source = Enumerable.Range(0, 100000).ToArray();

        // Partition the entire source array.
        var rangePartitioner = Partitioner.Create(0, source.Length);

        double[] results = new double[source.Length];

        // Loop over the partitions in parallel.
        Parallel.ForEach(rangePartitioner, (range, loopState) =&gt;
        {
            // Loop over each range element without a delegate invocation.
            for (int i = range.Item1; i &lt; range.Item2; i++)
            {
                results[i] = source[i] * Math.PI;
            }
        });

        Console.WriteLine(&quot;Operation complete. Print results? y/n&quot;);
        char input = Console.ReadKey().KeyChar;
        if (input == &apos;y&apos; || input == &apos;Y&apos;)
        {
            foreach(double d in results)
            {
                Console.Write(&quot;{0} &quot;, d);
            }           
        }
    }
}
</code></pre><h3 id="TPL-中的-Dataflow"><a href="#TPL-中的-Dataflow" class="headerlink" title="TPL 中的 Dataflow"></a>TPL 中的 Dataflow</h3><p>任务并行库（TPL）提供 Dataflow 来帮助提高启用并发功能的应用程序的健壮性。这些 Dataflow 统称为TPL数据流库。此数据流模型通过为粗粒度数据流和流水线任务提供进程内消息传递来促进基于角色的编程。数据流组件基于TPL的类型和调度基础结构，并与异步编程的C＃，Visual Basic和F＃语言支持集成。当您有多个必须异步通信或需要处理数据的数据流时，这些数据流组件非常有用。例如，考虑处理来自 webCam 的图像数据的应用程序。通过使用数据流模型，应用程序可以在图像帧可用时对其进行处理。如果应用程序增强了图像帧，例如通过执行光校正或减轻红眼，则可以创建数据流组件的管道。流水线的每个阶段都可能使用更多粗粒度并行功能，例如TPL提供的功能来转换图像。</p>
<p><a href="https://docs.microsoft.com/en-us/dotnet/standard/parallel-programming/dataflow-task-parallel-library" target="_blank" rel="noopener">Dataflow</a></p>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#NET-中的异步-多线程-并发处理-3-并行处理"><span class="nav-number">1.</span> <span class="nav-text">.NET 中的异步/多线程/并发处理(3) - 并行处理</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#并行处理"><span class="nav-number">1.1.</span> <span class="nav-text">并行处理</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#Task-Parallel-Library-TPL"><span class="nav-number">1.1.1.</span> <span class="nav-text">Task Parallel Library (TPL)</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#Data-Parallelism"><span class="nav-number">1.1.2.</span> <span class="nav-text">Data Parallelism</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#当循环体的计算比较简单时的处理办法"><span class="nav-number">1.1.2.1.</span> <span class="nav-text">当循环体的计算比较简单时的处理办法</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#TPL-中的-Dataflow"><span class="nav-number">1.1.3.</span> <span class="nav-text">TPL 中的 Dataflow</span></a></li></ol></li></ol></li></ol></div>
            

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